Exploring Preservice Science Teachers’ AI-TPACK Competencies and Perceptions of Artificial Intelligence in Education

Öz

The primary purpose of this study is to investigate preservice science teachers’ competencies related to AI-TPACK and their perceptions regarding the use of artificial intelligence in education. By examining these constructs, the study aims to provide insights into how preservice teachers integrate AI into their pedagogical and content knowledge and how their perceptions influence this process. This study adopted a quantitative survey research design. Specifically, two standardized instruments, the AI-TPACK and Perceptions of Artificial Intelligence in Education (PAI) scales, were administered simultaneously to preservice science teachers. The participants of this study consisted of 122 undergraduate students enrolled in the science education program at the faculty of education of a public university. The findings indicate that preservice science teachers’ overall performance on the AI-TPACK scale reflected moderate competency in integrating AI into pedagogical practices. The highest mean scores highlighted willingness to integrate AI technologies into self directed learning, self-efficacy in enhancing conceptual understanding, and competence in using AI-based simulations. The findings revealed that preservice science teachers’ overall results on the PAI indicated generally positive perceptions toward AI integration in education. Preservice science teachers particularly valued AI’s contribution to efficiency, rapid assessment, and ease of access to information while noting potential drawbacks such as reduced student engagement and dependency. The analysis revealed a statistically significant positive correlation between preservice teachers’ AI-TPACK competencies and their perceptions of AI. Results also indicated that male preservice teachers scored significantly higher in several AI-TPACK subscales. At the same time, prior AI training enhanced technological knowledge but did not necessarily improve pedagogical or content-related competencies. Overall, the findings highlight the need for targeted training opportunities emphasizing hands-on, pedagogically integrated use of AI to foster more comprehensive AI-TPACK competencies.

Anahtar Kelimeler

• Preservice Science Teachers
• Artificial Intelligence in Education
• AI-TPACK
• Perceptions of AI.

Fen Bilimleri Öğretmen Adaylarının YZ-TPAB Yeterlikleri ve Eğitimde Yapay Zekaya İlişkin Algılarının İncelenmesi

Öz

Bu çalışmanın temel amacı, fen bilimleri öğretmen adaylarının yapay zeka teknolojik pedagojik alan bilgisi (AI-TPACK) yeterliklerini ve eğitimde yapay zeka kullanımına ilişkin algılarını (PAI) incelemektir. Bu amaç doğrultusunda öğretmen adaylarının yapay zekayı teknolojik pedagojik alan bilgileriyle nasıl bütünleştirdikleri ve algılarının bu süreci nasıl etkilediğini incelemek hedeflenmiştir. Araştırmada nicel tarama deseninden yararlanılmıştır. Bu kapsamda, AI-TPACK ve PAI ölçekleri fen bilimleri öğretmen adaylarına eşzamanlı olarak uygulanmıştır. Araştırmanın çalışma grubunu, bir devlet üniversitesinin eğitim fakültesi fen bilimleri öğretmenliği programına kayıtlı 122 lisans öğrencisi oluşturmaktadır. Bulgular, fen bilgisi öğretmen adaylarının AI-TPACK ölçeğinde yapay zekayı teknolojik pedagojik alan uygulamalarına entegre etme konusunda orta düzeyde yeterlik sergilediklerini ortaya koymuştur. En yüksek ortalama puanlar, öğretmen adaylarının yapay zeka teknolojilerini öz düzenlemeli öğrenmeye entegre etme isteklilikleri, kavramsal anlamayı geliştirme konusundaki özyeterlikleri ve yapay zeka tabanlı simülasyonları kullanma becerileri üzerine yoğunlaşmıştır. Bulgular ayrıca, PAI ölçeği sonuçlarının öğretmen adaylarının genel olarak eğitimde yapay zeka entegrasyonuna ilişkin olumlu algılara sahip olduklarını göstermiştir. Fen bilimleri öğretmen adayları özellikle yapay zekanın verimlilik, hızlı değerlendirme ve bilgiye erişimde kolaylık sağlama katkılarını değerli bulurken; öğrenci katılımında azalma ve bağımlılık gibi potansiyel olumsuzlukları da vurgulamışlardır. Analizler, öğretmen adaylarının AI-TPACK yeterlikleri ile yapay zekaya ilişkin algıları arasında istatistiksel olarak anlamlı ve pozitif bir ilişki bulunduğunu ortaya koymuştur. Ayrıca, erkek öğretmen adaylarının AI-TPACK ölçeğinin bazı alt boyutlarında anlamlı biçimde daha yüksek puanlar aldıkları belirlenmiştir. Diğer taraftan, önceki yapay zeka eğitiminin teknolojik bilgiyi artırdığı, ancak pedagojik ya da alan bilgisi ile ilgili yeterlikleri yeterince geliştirmediği saptanmıştır. Genel olarak bulgular, AI-TPACK yeterliklerini daha kapsamlı biçimde geliştirmek için uygulamalı ve pedagojik bütünleşmeye dayalı branşa özel eğitim fırsatlarının gerekliliğine işaret etmektedir.

Anahtar Kelimeler

• Fen Bilimleri Öğretmen Adayları
• Eğitimde Yapay Zeka
• Yapay Zeka Teknolojik Pedagojik Alan Bilgisi (YZ-TPAB)
• Yapay Zekaya İlişkin Algı

Kaynakça

1. Abdelmoneim, R., Jebreen, K., Radwan, E., & Kammoun-Rebai, W. (2024). Perspectives of teachers on the employ of educational artificial intelligence tools in education: The case of the Gaza Strip, Palestine. Human Arenas, 1-30. https://doi.org/10.1007/s42087-024-00399-1
2. Alejandro, I. M. V., Sanchez, J. M. P., Sumalinog, G. G., Mananay, J. A., Goles, C. E., & Fernandez, C. B. (2024). Pre-service teachers’ technology acceptance of artificial intelligence (AI) applications in education. STEM Education, 4(4), 445-465. https://doi.org/10.3934/steme.2024024
3. Alm, A., & Ohashi, L. (2024). A worldwide Study on language educators’ initial response to ChatGPT. Technology in Language Teaching & Learning, 6(1), n1. 1141. https://doi.org/10.29140/tltl.v6n1.1141
4. Alwaqdani, M. (2025). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30(3), 2737-2755. https://doi.org/10.1007/s10639-024-12903-9
5. Bautista, A., Estrada, C., Jaravata, A. M., Mangaser, L. M., Narag, F., Soquila, R., & Asuncion, R. J. (2024). Preservice teachers' readiness towards integrating AI-based tools in education: A TPACK approach. Educational Process: International Journal, 13(3), 40-68. https://doi.org/10.22521/edupij.2024.133.3
6. Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Rivas-Manzano, M. d. R. d. (2024). Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Sciences, 14(7), 740. https://doi.org/10.3390/educsci14070740
7. Canbazoğlu Bilici, S., Tanrısevdi, M., Yıldız Durak, H., & Çakıroğlu, J. (2024, August 25-26). Yapay zeka teknolojik pedagojik alan bilgisi (YZ-TPAB) ölçeğinin Türkçeye uyarlaması [Paper presentation]. 5th International Conference on Engineering and Applied Natural Sciences, Konya, Türkiye.
8. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468

9. Chiu, T. K., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667-6685. https://doi.org/10.1007/s10639-024-13094-z
10. Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910-922. https://doi.org/10.1080/10447318.2022.2049145
11. Choudhury, S., Deb, J. P., Pradhan, P., & Mishra, A. (2024). Validation of the teachers AI-TPACK scale for the Indian educational setting. International Journal of Experimental Research and Review, 43, 119-133. https://doi.org/10.52756/ijerr.2024.v43spl.009
12. Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. Routledge.
13. Creswell, J. W. (2014). Research design: Qualitative, quantitative and mixed methods approaches (4th ed.). Sage.
14. Dogan, S., Nalbantoglu, U. Y., Celik, I., & Agacli Dogan, N. (2025). Artificial intelligence professional development: A systematic review of TPACK, designs, and effects for teacher learning. Professional Development in Education, 51(3), 519-546. https://doi.org/10.1080/19415257.2025.2454457
15. EU Commission, (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/153756
16. Fraenkel, J. R. & Wallen, N. E. (2009). How to design evaluate research in education (7th ed.). McGraw-Hill Companies
17. Gorsuch, R. L. (1983). Factor Analysis (2nd ed.). Erlbaum.
18. Hava, K., & Babayiğit, Ö. (2025). Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency. Education and Information Technologies, 30(3), 3491-3508. https://doi.org/10.1007/s10639-024-12939-x
19. Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
20. Işık, S., Çakır, R., & Korkmaz, Ö. (2024). Teachers' perception scale towards the use of artificial intelligence tools in education. Participatory Educational Research, 11(Prof. Dr. H. Ferhan Odabaşı Gift Issue), 80-94. http://dx.doi.org/10.17275/per.24.95.11.6
21. Karataş, F., & Ataç, B. A. (2024). When TPACK meets artificial intelligence: Analyzing TPACK and AI-TPACK components through structural equation modelling. Education and Information Technologies, 30, 8979–9004. https://doi.org/10.1007/s10639-024-13164-2
22. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
23. Koh, J. H. L., & Chai, C. S. (2014). Teacher clusters and their perceptions of technological pedagogical content knowledge (TPACK) development through ICT lesson design. Computers & Education, 70, 222-232. https://doi.org/10.1016/j.compedu.2013.08.017
24. Kotsis, K. T. (2025). Misconceptions about artificial intelligence from preservice teachers: A literature review. EIKI Journal of Effective Teaching Methods, 3(2), 199-208. https://doi.org/10.59652/jetm.v3i2.565
25. Lan, G., Feng, X., Du, S., Song, F., & Xiao, Q. (2025). Integrating ethical knowledge in generative AI education: Constructing the GenAI-TPACK framework for university teachers’ professional development. Education and Information Technologies, 30, 15621–15644. https://doi.org/10.1007/s10639-025-13427-6
26. Li, N., Lau, K. L., Liang, Y., & Chai, C. S. (2024). Pre-service foreign language teachers’ TPACK preparation for technology integration: What are the profiles and key drivers? Asia Pacific Journal of Education, 1–17. https://doi.org/10.1080/02188791.2024.2415392
27. Liu, Y., Kauttonen, J., Zhao, B., Li, X., & Peng, W. (2024). Towards Emotion AI to next generation healthcare and education. Frontiers in Psychology, 15, 1533053. https://doi.org/10.3389/fpsyg.2024.1533053
28. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record, 108(6), 1017-1054.
29. Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers and Education: Artificial Intelligence, 8, 100368. https://doi.org/10.1016/j.caeai.2025.100368
30. Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
31. Nissim, Y., & Simon, E. (2025). The diffusion of artificial intelligence innovation: Perspectives of preservice teachers on the integration of ChatGPT in education. Journal of Education for Teaching, 51(2), 381-401. https://doi.org/10.1080/02607476.2025.2469128
32. Oved, O., & Alt, D. (2025). Teachers’ technological pedagogical content knowledge (TPACK) as a precursor to their perceived adopting of educational AI tools for teaching purposes. Education and Information Technologies, 30, 14095–14121. https://doi.org/10.1007/s10639-025-13371-5
33. Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of pre-service teachers towards artificial intelligence (AI): The role of AI-related teacher training courses and AI-TPACK within the technology acceptance model. Education Sciences, 15(2), 167. https://doi.org/10.3390/educsci15020167
34. Sanusi, I. T., Agbo, F. J., Dada, O. A., Yunusa, A. A., Aruleba, K. D., Obaido, G., Olawumi, O., & Oyelere, S. S. (2024). Stakeholders’ insights on artificial intelligence education: Perspectives of teachers, students, and policymakers. Computers and Education Open, 7, 100212. https://doi.org/10.1016/j.caeo.2024.100212
35. Shafie, H., Abd Majid, F., & Sharil, W. N. E. H. (2023, September). Education qualifications and teaching experience on the 21st century TPACK level of English language. In Proceedings of the 4th International Conference on English Language Teaching (ICON-ELT 2023) (Vol. 780, p. 4). Springer Nature.
36. Spasopoulos, T., Sotiropoulos, D., & Kalogiannakis, M. (2025). Generative AI in pre-service science teacher education: A systematic review. Advances in Mobile Learning Educational Research, 5(2), 1501-1523. https://doi.org/10.25082/AMLER.2025.02.007
37. Tabachnick, B.G., Fidell, L.S. (2013). Using multivariate statistics (6th ed.). Allyn & Bacon.
38. Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931-939. https://doi.org/10.25082/AMLER.2024.01.005
39. Üzüm, B., Elçiçek, M., & Pesen, A. (2025). Development of teachers’ perception scale regarding artificial intelligence use in education: Validity and reliability study. International Journal of Human–Computer Interaction, 41(5), 2776-2787. https://doi.org/10.1080/10447318.2024.2385518
40. Velli, K., & Zafiropoulos, K. (2024). Factors that affect the acceptance of educational AI tools by Greek teachers - A structural equation modelling study. European Journal of Investigation in Health, Psychology and Education, 14(9), 2560-2579. https://doi.org/10.3390/ejihpe14090169
41. Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F. (2025). What explains teachers’ trust in AI in education across six countries? International Journal of Artificial Intelligence in Education, 35, 1288-1316. https://doi.org/10.1007/s40593-024-00433-x
42. Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7
43. Zhao, J., Liu, E., & Sofeia, N. (2024). Whether perceived TPACK could improve deep learning? Through the lens of the mediating role of self-regulatory learning and the moderating role of technology self-efficacy in the online environment. Current Psychology, 43(37), 29848-29864. https://doi.org/10.1007/s12144-024-06455-x